Gastos Casa
10 octubre, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 29/8/2022 | Netflix | 8320 | Tami | NA |
| 31/8/2022 | Incoludido | 21000 | Tami | NA |
| 31/8/2022 | Electricidad | 89272 | Andrés | PAC ENEL 01686518 |
| 31/8/2022 | Enceres | 12000 | Andrés | Visita gasfiter |
| 3/9/2022 | Comida | 59225 | Andrés | Lider |
| 3/9/2022 | Comida | 21350 | Andrés | Laflordeloto.cl |
| 5/9/2022 | Comida | 78035 | Tami | NA |
| 7/9/2022 | Enceres | 15400 | Andrés | era del mes pasado pero igual |
| 10/9/2022 | Agua | 10860 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 10/9/2022 | basureros | 20000 | Andrés | basureros |
| 10/9/2022 | Comida | 41870 | Andrés | NA |
| 12/9/2022 | Comida | 3020 | Andrés | Reste el costa rama |
| 12/9/2022 | Comida | 20563 | Andrés | Laflordeloto.cl |
| 12/9/2022 | Enceres | 57000 | Andrés | Arreglo wc+ visita |
| 15/9/2022 | Comida | 38863 | Tami | NA |
| 16/9/2022 | Diosi | 12136 | Tami | Pipeta antipulgas |
| 19/9/2022 | Comida | 5070 | Andrés | NA |
| 20/9/2022 | Comida | 62208 | Tami | NA |
| 21/9/2022 | VTR | 17990 | Andrés | Entel con mercadopago |
| 28/9/2022 | Comida | 79070 | Tami | NA |
| 30/9/2022 | Netflix | 8402 | Tami | NA |
| 30/9/2022 | Comida | 28000 | Andrés | Caramagnola |
| 2/10/2022 | Enceres | 10000 | Andrés | Manguera |
| 2/10/2022 | Electricidad | 48087 | Andrés | atrasado del mes anterior |
| 4/10/2022 | Comida | 41760 | Andrés | NA |
| 4/10/2022 | Comida | 12860 | Andrés | NA |
| 8/10/2022 | Brussels | 25300 | Tami | NA |
| 8/10/2022 | Comida | 25300 | Tami | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.4356e+08 2 4.9343 0.0076 **
## lag_depvar 7.6788e+10 1 1708.4289 <2e-16 ***
## Residuals 2.2338e+10 497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 926.4644 13531.21 0.0198028
## 2-0 27476.828 21668.0419 33285.61 0.0000000
## 2-1 20247.990 16733.1141 23762.87 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
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## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 345 49711.09 15965.681
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2057.549133 4056.939419 -554.482462 2423.845052 -3002.082751
## 7 8 9 10 11
## 506.951006 -5673.190557 -1168.117290 -3944.392405 -375.549148
## 12 13 14 15 16
## -4903.336942 -1547.572816 -838.297214 433.788855 -3199.722390
## 17 18 19 20 21
## -321.657979 -2081.880226 6657.250549 -1531.338322 -1203.331515
## 22 23 24 25 26
## 1484.610303 -1192.332898 234.159538 1689.449045 -7121.940851
## 27 28 29 30 31
## 975.026651 8206.555902 371.522029 -61.030132 -2444.999986
## 32 33 34 35 36
## 1550.289409 4535.737687 1060.317926 2322.203075 -1948.142313
## 37 38 39 40 41
## 4547.202617 4267.942028 -2336.220543 -3019.109137 -1123.069479
## 42 43 44 45 46
## -10745.478355 7358.563858 2567.862286 1358.431646 8088.231025
## 47 48 49 50 51
## 615.975461 6462.500431 6611.844221 -6017.855691 -4874.129337
## 52 53 54 55 56
## -5096.148609 -7926.254167 6185.471749 -4069.946578 -4861.075702
## 57 58 59 60 61
## 3917.843442 915.510566 -14.275984 157.728037 -4984.154743
## 62 63 64 65 66
## 18171.189837 3555.914812 -3745.176431 5863.360649 7249.368624
## 67 68 69 70 71
## 14506.052219 1477.460991 -13412.883065 -1391.280683 4577.844974
## 72 73 74 75 76
## -4989.442380 -4449.258801 -10506.683394 2530.287987 -5361.206726
## 77 78 79 80 81
## 1134.193578 -6812.109274 640.984167 -2276.253854 -2609.491937
## 82 83 84 85 86
## -3839.888293 -430.411194 2411.719910 3831.483522 511.000712
## 87 88 89 90 91
## -458.341949 222.496324 4322.892909 -1174.425796 1148.526920
## 92 93 94 95 96
## -2074.662912 -1039.370989 188.742548 283.035647 -7478.991742
## 97 98 99 100 101
## 2447.178212 -8570.644115 -2853.912059 -3943.607256 -1625.268835
## 102 103 104 105 106
## -1152.032604 3285.078127 -2273.046196 2670.178131 -1109.876376
## 107 108 109 110 111
## 1020.573842 2623.980927 -3140.159719 -4689.233926 -788.527933
## 112 113 114 115 116
## 1963.098125 11732.326027 -1289.162183 2636.101227 4215.980605
## 117 118 119 120 121
## 3432.302348 -1185.639240 -4783.857328 -3750.941848 2321.675609
## 122 123 124 125 126
## -1747.070504 1339.521336 8848.079469 777.299522 63.455546
## 127 128 129 130 131
## -2580.892093 2620.056322 7003.532537 921.091568 -8586.370992
## 132 133 134 135 136
## 1731.246511 4107.604571 -3216.921274 -1444.556181 -866.159767
## 137 138 139 140 141
## -3885.016572 1205.060706 -484.568644 -2900.897333 1749.023324
## 142 143 144 145 146
## -1866.130061 -7803.576050 2115.398080 -3427.595947 2171.361425
## 147 148 149 150 151
## -211.786506 1064.410961 -330.465708 1379.550923 1200.957470
## 152 153 154 155 156
## 3360.654071 -4881.546896 -1158.659361 -3214.204994 5997.529016
## 157 158 159 160 161
## 9741.158630 -3107.451836 -4440.560876 3962.745588 514.144405
## 162 163 164 165 166
## 3004.428060 -5629.491602 -6430.612288 4510.724181 17703.016835
## 167 168 169 170 171
## 3800.794796 -244.717633 -2279.655790 -912.015930 3795.445712
## 172 173 174 175 176
## -46.208937 -7885.813985 3116.773382 4554.638738 823.066356
## 177 178 179 180 181
## 8947.153281 -9111.824880 -3256.318645 -10502.462270 -10921.673377
## 182 183 184 185 186
## 1623.648818 9652.630766 -1155.550581 6207.134972 6782.491670
## 187 188 189 190 191
## 13334.462603 8511.396097 -4032.192168 2545.717670 10444.285714
## 192 193 194 195 196
## -1632.688196 -2399.190906 -10198.537472 -6188.948394 1459.787833
## 197 198 199 200 201
## -5018.923257 -9542.449882 5706.864988 -2799.157998 -1426.600723
## 202 203 204 205 206
## -514.058446 6781.002772 10107.569179 722.220133 3070.048732
## 207 208 209 210 211
## 3226.676164 5897.304342 12911.935258 -5694.404532 -11233.264655
## 212 213 214 215 216
## -5497.912426 -10369.693695 -4775.549559 1855.699051 -12707.369241
## 217 218 219 220 221
## 16783.421463 8052.474779 1704.717224 26855.641079 12485.218423
## 222 223 224 225 226
## 7222.546300 13893.066097 -4118.587435 -1866.220385 3705.471236
## 227 228 229 230 231
## 292.073019 2709.422865 8977.302510 5763.572641 -1981.624496
## 232 233 234 235 236
## -1851.800428 9445.950131 -11538.129873 -7193.612434 -8379.797554
## 237 238 239 240 241
## -9867.569191 3387.481233 1625.854910 -8041.486895 -8674.192606
## 242 243 244 245 246
## 9468.082933 -7483.507996 2821.555776 -10001.785421 -3684.336730
## 247 248 249 250 251
## 1804.584525 1352.221045 -11992.291026 4050.059254 2415.404229
## 252 253 254 255 256
## 4529.718837 2404.522897 -916.697926 11386.554269 21031.486519
## 257 258 259 260 261
## 3180.646803 -4290.333372 4149.546777 -1670.447808 3794.843647
## 262 263 264 265 266
## -4808.741097 -10793.364621 -4528.564562 -283.984009 -4951.805184
## 267 268 269 270 271
## 9051.332506 -4090.500766 4413.505442 -1922.863870 4631.840044
## 272 273 274 275 276
## 870.342167 7460.209799 -1314.173803 12143.637627 -4562.218217
## 277 278 279 280 281
## 1802.700498 -298.892577 7939.429851 -5026.889041 -2639.943425
## 282 283 284 285 286
## -11135.642764 -2438.481203 18903.925184 7862.320057 2756.076315
## 287 288 289 290 291
## -612.844672 945.799119 6445.837664 6888.555694 -18807.120541
## 292 293 294 295 296
## -10979.913462 -7857.367508 9995.331726 3301.202789 -981.213828
## 297 298 299 300 301
## 27610.170335 10023.368731 4794.005282 9400.929087 2688.899093
## 302 303 304 305 306
## -1184.053266 7794.595421 -24434.368450 -3409.471294 -6.816028
## 307 308 309 310 311
## -6792.562500 -3726.753411 3211.310611 -8947.002192 -2901.600019
## 312 313 314 315 316
## -7838.891028 1976.009175 -2776.406336 2434.898307 -3734.964108
## 317 318 319 320 321
## 27815.399722 -651.598362 3383.336637 10902.280586 5572.062334
## 322 323 324 325 326
## 32335.057439 4789.301090 -21243.186660 1746.783608 1078.269614
## 327 328 329 330 331
## -6478.854185 -1659.489434 -33159.485176 1357.526232 -1856.850256
## 332 333 334 335 336
## 355.766018 -2737.587929 4528.647565 -53.935345 -6578.565681
## 337 338 339 340 341
## -2686.806971 -1749.841616 -7236.108808 4350.258792 -938.059513
## 342 343 344 345 346
## -1311.001072 -568.979326 592.524247 877.791163 -1243.300618
## 347 348 349 350 351
## -9069.512863 -12754.224466 2872.929473 -3819.059875 -3140.090181
## 352 353 354 355 356
## -5455.380245 2303.707534 1884.478681 3209.366002 -3363.700099
## 357 358 359 360 361
## -95.250908 1082.207691 7392.065213 566.931238 242.720685
## 362 363 364 365 366
## 2859.128217 -2504.939757 -603.739973 -8463.194312 -4257.536620
## 367 368 369 370 371
## -5807.925831 -4496.635451 -6770.098897 5547.053014 817.828750
## 372 373 374 375 376
## 7539.259712 -7311.462103 -1866.881532 -2983.977852 -2044.231988
## 377 378 379 380 381
## -12027.490174 2442.899995 -10146.787814 6267.254012 9809.851242
## 382 383 384 385 386
## 3474.216104 -2096.833825 1923.854444 7036.520063 11624.285907
## 387 388 389 390 391
## -5709.856985 -5198.438688 67.835040 8789.200155 1948.649402
## 392 393 394 395 396
## 11344.579416 -9866.145961 2910.662130 827.437484 680.055779
## 397 398 399 400 401
## -531.742565 -424.027156 -14333.787980 8844.086054 -955.833724
## 402 403 404 405 406
## -1131.535132 7238.663597 -7751.958983 -1022.206405 -2243.314170
## 407 408 409 410 411
## -5504.917616 -2485.477941 -3523.079605 -8332.015782 6634.575090
## 412 413 414 415 416
## 2047.559947 -7001.652535 -7253.361639 14723.180507 4131.616661
## 417 418 419 420 421
## 4753.595484 -7828.883852 -4445.584368 -2254.598851 3185.945048
## 422 423 424 425 426
## -13687.366540 -2321.947819 -8621.371653 3565.849059 7463.662792
## 427 428 429 430 431
## 6958.075176 -3693.192858 -3787.947334 -4352.702506 -1380.777748
## 432 433 434 435 436
## -5300.196069 -6169.961641 -5443.242013 -850.407912 -322.228111
## 437 438 439 440 441
## -4470.997790 3111.396563 5309.488855 -4664.580072 -1726.981783
## 442 443 444 445 446
## 2011.137645 -3439.843981 3258.497148 -6204.890616 -11679.108388
## 447 448 449 450 451
## -3968.979619 10204.339335 -1609.950124 5180.542083 -5512.542032
## 452 453 454 455 456
## -712.728364 789.898699 3412.719198 -11927.732023 3832.186590
## 457 458 459 460 461
## -6296.165524 6982.991961 3381.783512 2832.973545 -3553.007782
## 462 463 464 465 466
## 2423.899200 294.695117 2091.514172 -244.949570 3631.555993
## 467 468 469 470 471
## -2394.841127 6081.293170 -6727.188744 -2667.012769 -1876.977887
## 472 473 474 475 476
## -4315.994628 3387.254748 8144.414845 -5759.947976 1808.522790
## 477 478 479 480 481
## -5874.353550 -2476.299253 2401.097769 -12573.581284 -9271.949092
## 482 483 484 485 486
## -640.852587 561.676923 -451.333386 -847.774198 -9101.533030
## 487 488 489 490 491
## 11654.246420 6646.121226 7750.748492 -5189.672840 5674.954239
## 492 493 494 495 496
## 9541.385819 6208.579926 -13367.707488 -10298.076273 -3059.895314
## 497 498 499 500 501
## -698.722054 -118.852132 -7228.050304 1076.586698 4727.184363
## 502
## 5886.616042
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17211.74 20082.06 24370.63 24086.30 26458.80 23769.76 24491.90 19685.26
## 10 11 12 13 14 15 16 17
## 19419.68 16740.83 17524.62 14227.43 14279.01 14949.07 16659.44 14965.80
## 18 19 20 21 22 23 24 25
## 16008.88 15377.32 22517.34 21593.90 21069.53 22974.90 22295.41 22953.27
## 26 27 28 29 30 31 32 33
## 24814.23 18693.26 20433.44 28334.48 28392.60 28062.86 25673.00 27086.83
## 34 35 36 37 38 39 40 41
## 30961.11 31312.37 32733.00 30223.37 34175.06 37409.22 34441.39 31226.36
## 42 43 44 45 46 47 48 49
## 30064.76 20567.72 28147.57 30603.85 31701.91 38595.60 38086.07 42786.16
## 50 51 52 53 54 55 56 57
## 47056.86 39695.42 34219.72 29201.97 22290.67 28631.80 25184.65 21452.16
## 58 59 60 61 62 63 64 65
## 25896.35 27166.13 27465.56 27880.73 23718.10 40444.23 42303.18 37510.50
## 66 67 68 69 70 71 72 73
## 41751.63 46707.23 57462.11 55459.74 40582.99 38068.58 41111.01 35364.83
## 74 75 76 77 78 79 80 81
## 30780.11 21408.00 24635.49 20528.09 22631.11 17485.16 19516.97 18737.21
## 82 83 84 85 86 87 88 89
## 17757.03 15810.27 17098.42 20735.80 25189.43 26187.34 26212.50 26834.25
## 90 91 92 93 94 95 96 97
## 30992.85 29813.90 30821.38 28870.09 28063.40 28434.54 28844.42 22369.68
## 98 99 100 101 102 103 104 105
## 25409.22 18383.05 17229.89 15254.70 15556.89 16239.78 20748.76 19824.82
## 106 107 108 109 110 111 112 113
## 23364.45 23152.71 24842.45 27742.59 25220.38 21634.96 21912.62 24580.39
## 114 115 116 117 118 119 120 121
## 35533.16 33711.33 35563.73 38586.41 40558.21 38227.86 33006.80 29318.47
## 122 123 124 125 126 127 128 129
## 31418.21 29684.19 30875.35 38536.84 38176.40 37230.32 34068.37 35864.04
## 130 131 132 133 134 135 136 137
## 41305.77 40741.51 31871.75 33146.82 36362.49 32743.98 31118.16 30195.73
## 138 139 140 141 142 143 144 145
## 26724.80 28150.71 27918.47 25585.98 27626.84 26240.43 19790.60 22845.74
## 146 147 148 149 150 151 152 153
## 20654.78 23656.07 24200.45 25803.75 25987.31 27654.90 28966.20 32022.98
## 154 155 156 157 158 159 160 161
## 27456.37 26713.35 24248.76 30190.70 41127.88 39444.56 36788.11 41849.14
## 162 163 164 165 166 167 168 169
## 43269.14 46712.78 42141.90 37410.99 42880.27 59314.78 61544.86 59946.08
## 170 171 172 173 174 175 176 177
## 56746.02 55132.27 57856.78 56872.96 49102.51 51948.93 55721.93 55758.42
## 178 179 180 181 182 183 184 185
## 62945.11 53370.32 50094.89 40828.96 32299.64 35836.37 46021.84 45473.44
## 186 187 188 189 190 191 192 193
## 51474.51 57266.11 68136.60 73462.34 67105.85 67300.86 74428.55 70069.91
## 194 195 196 197 198 199 200 201
## 65556.39 54712.95 48694.64 50130.49 45689.45 37794.71 44271.59 42484.60
## 202 203 204 205 206 207 208 209
## 42119.63 42601.85 49451.00 58412.35 58038.95 59777.75 61446.98 65268.92
## 210 211 212 213 214 215 216 217
## 74812.26 66830.84 54924.06 49489.12 40412.41 37345.44 40484.37 30423.58
## 218 219 220 221 222 223 224 225
## 47534.81 54915.00 55824.22 78774.35 86330.17 88349.65 96002.59 86880.08
## 226 227 228 229 230 231 232 233
## 80829.81 80408.36 77031.15 76185.84 80961.28 82336.62 76726.94 71901.05
## 234 235 236 237 238 239 240 241
## 77600.56 64140.04 56111.94 47997.28 39540.80 43766.72 45936.92 39334.48
## 242 243 244 245 246 247 248 249
## 32962.77 43328.65 37528.87 41496.50 33697.62 32392.99 36077.92 38924.72
## 250 251 252 253 254 255 256 257
## 29679.80 35666.02 39498.28 44735.19 47475.56 46964.02 57348.51 74987.64
## 258 259 260 261 262 263 264 265
## 74801.19 68057.60 69551.45 65741.58 67199.46 60906.51 50094.14 46089.27
## 266 267 268 269 270 271 272 273
## 46300.38 42375.52 51251.07 47493.92 51674.29 49775.59 53875.94 54174.36
## 274 275 276 277 278 279 280 281
## 60240.60 57855.65 67607.08 61482.59 61694.32 60030.00 65819.46 59499.09
## 282 283 284 285 286 287 288 289
## 56035.07 45502.62 43886.36 61258.39 66833.35 67246.13 64642.77 63722.73
## 290 291 292 293 294 295 296 297
## 67756.16 71698.12 52540.48 42562.22 36524.67 46929.80 50197.93 49304.69
## 298 299 300 301 302 303 304 305
## 73697.35 79690.99 80364.07 85013.96 83197.91 78187.83 81682.80 56377.90
## 306 307 308 309 310 311 312 313
## 52608.67 52285.85 46025.61 43212.40 46845.00 39336.74 38048.46 32565.85
## 314 315 316 317 318 319 320 321
## 36381.12 35555.82 39418.39 37386.46 63382.17 61205.81 62842.58 70905.65
## 322 323 324 325 326 327 328 329
## 73312.37 99000.98 97365.47 72999.36 71787.44 70131.43 62017.78 59116.63
## 330 331 332 333 334 335 336 337
## 28820.90 32538.42 32981.52 35320.30 34655.78 40469.65 41553.99 36762.95
## 338 339 340 341 342 343 344 345
## 35970.98 36098.68 31379.60 37427.35 38096.14 38356.69 39239.62 41040.07
## 346 347 348 349 350 351 352 353
## 42876.87 42626.51 35513.80 26004.93 31393.06 30244.80 29831.52 27428.58
## 354 355 356 357 358 359 360 361
## 32145.52 35930.35 40430.27 38604.54 39875.08 42030.93 49486.35 50041.42
## 362 363 364 365 366 367 368 369
## 50244.73 52727.94 50190.88 49630.91 42216.25 39390.21 35536.06 33296.67
## 370 371 372 373 374 375 376 377
## 29322.38 36669.60 38975.17 46924.89 40847.45 40290.12 38815.52 38344.49
## 378 379 380 381 382 383 384 385
## 29137.81 33773.36 26768.46 35054.72 45471.93 49066.41 47325.72 49333.62
## 386 387 388 389 390 391 392 393
## 55604.43 65167.14 58323.15 52746.31 52472.80 59912.49 60440.13 69179.43
## 394 395 396 397 398 399 400 401
## 58196.34 59775.99 59332.52 58812.17 57286.74 56038.22 42688.91 51344.55
## 402 403 404 405 406 407 408 409
## 50336.82 49294.62 55748.10 48229.78 47535.31 45848.35 41490.34 40311.51
## 410 411 412 413 414 415 416 417
## 38359.59 32405.57 40342.58 43292.80 37921.65 32969.82 47962.81 51838.98
## 418 419 420 421 422 423 424 425
## 55800.31 48208.01 44501.31 43166.48 46782.22 35106.80 34833.80 29045.72
## 426 427 428 429 430 431 432 433
## 34681.19 43076.78 50025.19 46764.23 43808.99 40709.06 40596.34 37045.39
## 434 435 436 437 438 439 440 441
## 33152.24 30363.69 31952.66 33817.14 31805.46 36711.37 42967.58 39693.41
## 442 443 444 445 446 447 448 449
## 39397.01 42427.99 40296.79 44318.89 39526.97 30485.98 29313.95 40763.66
## 450 451 452 453 454 455 456 457
## 40442.60 46139.97 41740.44 42092.96 43726.71 47475.30 37266.81 42155.74
## 458 459 460 461 462 463 464 465
## 37541.58 45172.50 48721.31 51363.29 48066.10 50426.02 50629.20 52390.52
## 466 467 468 469 470 471 472 473
## 51884.02 54851.84 52158.28 57250.76 50455.58 48046.98 46621.57 43218.32
## 474 475 476 477 478 479 480 481
## 47005.16 54529.52 48910.91 50628.07 45374.30 43740.05 46596.15 35923.81
## 482 483 484 485 486 487 488 489
## 29432.71 31317.32 34036.05 35538.20 36511.96 30100.75 42733.45 49448.11
## 490 491 492 493 494 495 496 497
## 56334.24 51002.47 55875.04 63571.13 67413.71 53557.65 44058.47 42067.29
## 498 499 500 501 502
## 42393.14 43190.76 37632.41 40050.96 45395.81
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8523
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.934335 0.572107 2.962175
## t2* 1708.428880 27.224608 245.495182
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.474269 5.087471 10.94558
## 2 lag_depvar 1363.301631 1720.173679 2161.50987Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Oct 10 01:04:20 2022
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# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 6.092444 | 5.953429 | 7.402273 |
| Comida | NA | 301.994333 | 311.081333 | 340.845818 |
| Comunicaciones | NA | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | NA | 43.415778 | 35.477048 | 30.068879 |
| Enceres | NA | 20.355000 | 17.181381 | 25.119788 |
| Farmacia | NA | 2.442222 | 9.044429 | 10.859818 |
| Gas/Bencina | NA | 50.616667 | 29.454952 | 25.019818 |
| Diosi | NA | 16.129222 | 37.019857 | 37.056758 |
| donaciones/regalos | NA | 0.000000 | 8.194381 | 8.324818 |
| Electrodomésticos/ Mantención casa | NA | 5.258667 | 34.593714 | 25.135394 |
| VTR | NA | 26.212222 | 22.140619 | 21.039939 |
| Netflix | NA | 7.695444 | 7.314476 | 7.653667 |
| Otros | NA | 4.201444 | 1.800619 | 1.145849 |
| Total | 0 | 484.413444 | 519.256238 | 539.672818 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1743, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-10-09 00:04:58 sería de: 36.024 pesos// Percentil 95% más alto proyectado: 39.527,33
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 34620.70 | 34573.97 |
| Lo.80 | 34807.56 | 34841.43 |
| Point.Forecast | 36024.04 | 38454.70 |
| Hi.80 | 37925.43 | 43096.21 |
| Hi.95 | 38971.37 | 45553.27 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3074 989.6778
## s.e. 0.1493 35.6567
##
## sigma^2 = 28420: log likelihood = -287.07
## AIC=580.13 AICc=580.73 BIC=585.49paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.3521 33.4775
## s.e. 0.1428 1.2494
##
## sigma^2 = 26998: log likelihood = -285.95
## AIC=577.91 AICc=578.51 BIC=583.26forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 960.774 | 642.4514 | 752.2061 |
| Lo.80 | 1079.871 | 762.6386 | 835.2950 |
| Point.Forecast | 1304.851 | 989.6776 | 1018.1159 |
| Hi.80 | 1529.831 | 1216.7166 | 1240.9507 |
| Hi.95 | 1648.928 | 1336.9038 | 1378.0265 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Andrés | Tami |
|---|---|---|---|
| 1 | marzo_2019 | 68268 | 175533 |
| 2 | abril_2019 | 55031 | 152640 |
| 3 | mayo_2019 | 192219 | 152985 |
| 4 | junio_2019 | 84961 | 291067 |
| 5 | julio_2019 | 205893 | 241389 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.2.7 bsts_0.9.8 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.10 MASS_7.3-54 scales_1.2.1
## [7] ggiraph_0.8.3 tidytext_0.3.4 DT_0.25
## [10] autoplotly_0.1.4 rvest_1.0.3 plotly_4.10.0
## [13] xts_0.12.1 forecast_8.18 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.0 tm_0.7-8
## [19] NLP_0.2-1 tsibble_1.1.3 forcats_0.5.2
## [22] dplyr_1.0.10 purrr_0.3.5 tidyr_1.2.1
## [25] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
## [28] sjPlot_2.8.11 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.8 httr_1.4.4
## [34] readxl_1.4.1 zoo_1.8-11 stringr_1.4.1
## [37] stringi_1.7.8 DataExplorer_0.8.2 data.table_1.14.2
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.7
## [43] readr_2.1.3
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 lme4_1.1-30
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] effectsize_0.8.0 its.analysis_1.6.0 withr_2.5.0
## [10] colorspace_2.0-3 ggfortify_0.4.14 highr_0.9
## [13] knitr_1.40 uuid_1.1-0 rstudioapi_0.14
## [16] TTR_0.24.3 labeling_0.4.2 emmeans_1.8.1-1
## [19] slam_0.1-50 bit64_4.0.5 farver_2.1.1
## [22] datawizard_0.6.2 fBasics_4021.92 rprojroot_2.0.3
## [25] vctrs_0.4.2 generics_0.1.3 xfun_0.33
## [28] R6_2.5.1 bitops_1.0-7 cachem_1.0.6
## [31] assertthat_0.2.1 networkD3_0.4 vroom_1.6.0
## [34] nnet_7.3-16 googlesheets4_1.0.1 gtable_0.3.1
## [37] spatial_7.3-14 timeDate_4021.106 rlang_1.0.6
## [40] forge_0.2.0 systemfonts_1.0.4 splines_4.1.2
## [43] lazyeval_0.2.2 gargle_1.2.1 selectr_0.4-2
## [46] broom_1.0.1 yaml_2.3.5 abind_1.4-5
## [49] modelr_0.1.9 crosstalk_1.2.0 backports_1.4.1
## [52] quantmod_0.4.20 tokenizers_0.2.3 tools_4.1.2
## [55] ellipsis_0.3.2 gplots_3.1.3 jquerylib_0.1.4
## [58] Rcpp_1.0.9 base64enc_0.1-3 fracdiff_1.5-1
## [61] haven_2.5.1 fs_1.5.2 magrittr_2.0.3
## [64] timeSeries_4021.104 lmtest_0.9-40 reprex_2.0.2
## [67] googledrive_2.0.0 mvtnorm_1.1-3 sjmisc_2.8.9
## [70] hms_1.1.2 evaluate_0.17 xtable_1.8-4
## [73] sjstats_0.18.1 ggeffects_1.1.3 compiler_4.1.2
## [76] KernSmooth_2.23-20 crayon_1.5.2 minqa_1.2.4
## [79] htmltools_0.5.3 tzdb_0.3.0 lubridate_1.8.0
## [82] DBI_1.1.3 sjlabelled_1.2.0 dbplyr_2.2.1
## [85] boot_1.3-28 Matrix_1.5-1 car_3.1-0
## [88] cli_3.4.1 quadprog_1.5-8 parallel_4.1.2
## [91] insight_0.18.4 igraph_1.3.5 pkgconfig_2.0.3
## [94] xml2_1.3.3 bslib_0.4.0 estimability_1.4.1
## [97] anytime_0.3.9 snakecase_0.11.0 janeaustenr_1.0.0
## [100] digest_0.6.29 parameters_0.19.0 janitor_2.1.0
## [103] rmarkdown_2.17 cellranger_1.1.0 curl_4.3.3
## [106] gtools_3.9.3 urca_1.3-3 nloptr_2.0.3
## [109] lifecycle_1.0.3 nlme_3.1-153 jsonlite_1.8.2
## [112] tseries_0.10-51 carData_3.0-5 viridisLite_0.4.1
## [115] fansi_1.0.3 pillar_1.8.1 fastmap_1.1.0
## [118] glue_1.6.2 bayestestR_0.13.0 bit_4.0.4
## [121] sass_0.4.2 performance_0.10.0 r2d3_0.2.6
## [124] caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))